What if one trading system only used on average of $30,000 in margin while the other used an average of $70,000? Theoretically you could have funded the first account with less money and invested the remaining dollars elsewhere. As a result, I am suggesting that you consider viewing returns as based of off the account size required as opposed to an arbitrary number. This could allow you to specify an amount by which to fund an account. In other words, you may trade the account as though it were a 100k account, but only put up $30,000 in trading funds because that is all you think you need. Many trading systems, money managers and CTA’s allow for such a notional funding approach. It can be an efficient use of capital.

The commercial testing software Trading Recipes has an intriguing way of computing these numbers. It can run a worse-case analysis. What this does is looks at every possible start date over time (for example 10 years). If there were 1000 different trades you could have started trading from, then it (Trading Recipes) tests the trading system from every single one of those 1000 trades. It then sums the worst drawdown and the required margin starting from each one of those 1000 trades, and it goes on to show how the system performed over the next 12 months. This allows you to create a frequency distribution of yearly returns verses account size required.

For the sake of this example, I am going to test 4 different trading systems and compare the results. The portfolio is 15 diversified markets that are all reasonably high in liquidity. Also, I have approximated margin based on 2 times the 5 day average true range multiplied by the point value, and then took the average of that over a period of 5 years. I’ve done it this way because margins change quite dramatically over time. Changing margins are typically caused by changes in a market’s underlying volatility. Computing this way will cause the margin to increase during higher volatility periods, and decrease during lower volatility periods. This risk adjusted method is similar to real life margin requirements. I think this is far more robust than to use the current margin values because past margin were likely different. Even though these may not be the exact margin amounts the above formula does seem to be close to the current margin levels in many commodities. You could always use a higher multiple if you wanted a greater “cushion”. This is just for demonstration purposes; decide the best way for you to simulate past and future margins in your testing. It would be terrific if data vendors like CSI sold data files for trading systems with the exact exchange minimum margin requirements throughout history, although I’ve not seen it.

Trading Systems Comparisons

For all tests:

Period tested was: 1/1/90 through 12/31/2003
Data: CSI backadjusted contracts
Slippage and commissions: $75
Starting Capital: $100,000

Money Management: Risk 2% of equity a trade or one contract if risk is less than $3000 (whichever is greater).

15 market portfolio: Euro Currency, Corn, Kansas City Wheat, Cotton, Sugar, Coffee, Crude Oil, Natural Gas, Japanese Yen, Swiss Franc, Five Year Notes, Thirty Year Bonds, Nikkei Index, London Nickel and London Copper

In the first test, we will use a Channel Breakout System similar to the “Turtle” method of trading. Specifically, this system buys at the highest price of the last 20 days and sells at the lowest price of the last 20 days. It then exits at the lowest price of the last 10 days for long positions and the highest price of the last 10 days for short positions. Risk computations are as a multiple of average true range, and protective stops get placed at those same levels.

Channel Breakout (20/10)

Starting periods available to test since 1990: 2080
Average required account size: $62,026.00**
Average first year profit: $39,086
Ratio of average account size required to average first year profit: 0.63

In this next test we use the same exact rules as above except the input values change to 50 and 20 (From 20 and 10)

Channel Breakout (50/20)
Starting periods available to test since 1990: 1017
Average required account size: $35,009.00**
Average first year profit: $52,341.00
Ratio of average account size required to average first year profit: 1.49

Aberration Trading System:
Starting periods available to test since 1990: 472
Average required account size: $12,502.00**
Average first year profit: $23,148.00
Ratio of average account size required to average first year profit: 1.85

Checkmate Trading System
Starting periods available to test since 1990: 551
Average required account Size: $15,922.00**
Average first year profit: $39,659.00
Ratio of average account size required to average first year profit: 2.49

Synergy Trading System
Starting periods available to test since 1990: 536
Average required account size: $17,358.00**
Average first year profit: $52,196
Ratio of average account size required to average first year profit: 3.00

**(Margins were approximated, they could be significantly higher or lower)

Trading Systems Results Summary

Here, you can see an intriguing phenomenon. The average first year profits for the Channel Breakout (20/10) were almost the same as Checkmate. Yet the average required account size for Checkmate was less than half. Similarly, the average first year profits for Channel Breakout (50/20) were almost the same as Synergy. Yet the average required account size for Synergy was again about half.

This can be valuable information for somebody who wants a notionally fund an account. If nothing else, it is an eye-opening perspective of how two trading systems can produce almost the same profit in a year given the same account size and money management, yet one of those trading systems can boast a much lower historically required account size. A reversal system or a short-term system like the (20/10) Channel Breakout will likely have higher requirements because of a greater number of simultaneous trades.

We have done these tests on many other trading systems. If you would like to see those reports, or if you would like the complete spreadsheet reports used in generating these tables please contact us.

www.RelativityTradingSystem.com

Dean Hoffman

CFTC REQUIRED RISK DISCLOSURE

HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH ARE DESCRIBED BELOW. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN. IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY ACHIEVED BY ANY PARTICULAR TRADING PROGRAM.

ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND NO HYPOTHETICAL TRADING RECORD CAN COMPLETELY ACCOUNT FOR THE IMPACT OF FINANCIAL RISK IN ACTUAL TRADING. FOR EXAMPLE, THE ABILITY TO WITHSTAND LOSSES OR TO ADHERE TO A PARTICULAR TRADING PROGRAM IN SPITE OF TRADING LOSSES ARE MATERIAL POINTS WHICH CAN ALSO ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF ANY SPECIFIC TRADING PROGRAM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF HYPOTHETICAL PERFORMANCE RESULTS AND ALL OF WHICH CAN ADVERSELY AFFECT ACTUAL TRADING RESULTS.

DH Trading Systems

A big problem with many trading systems is that they risk too much of a traders equity on any given trade. Most professionals agree that traders should never risk more than 1% to 3% of their equity on any given trade. This also applies to the risk for each sector. For example, if a trader is risking 2% a trade in highly correlated markets like 2yr bonds, 5yr bonds, 10yr bonds and 30yr bonds, this is essentially like risking 8% in the same trade. Overtrading this way can produce incredible looking results with returns of 100% or more, but this is usually just a case of using too much leverage and taking too large a percentage risk on each trade (or sector) and or “cherry picking” the best starting date (like right before a series of winning trades).

When running a “Worse Case Analysis” at those high-risk levels, it becomes clear that the risk of ruin climbs dangerously high. A series of losing trades or starting on the wrong day could cause an investor to lose it all (or have an enormous drawdown).

Trading System & Position Sizing Basics

The bottom line is that when putting on a trade, traders should know what percentage of their equity they will lose if they are wrong. This should only be a small portion of their available trading capital. This also means they need to know their risk when entering a trade. Some trading systems like moving average systems do not know how much risk they are taking. This is because the trading system does not know how far the market needs to move to trigger an exit. We think it is dangerous to trade this way and do not recommend it.

Another large problem is the lack of market normalization (such as a single contract based result). For example, we do not think it is logical to trade one contract of natural gas with an average daily volatility of around $2,000 for every one Eurodollar contract with an average daily volatility of around $150. Doing this would mean that natural gas is a more significant market than the Eurodollar. If Eurodollars trend, we want to give them just as much weight as natural gas (or any other market). In the previous example, traders could just simply remove the Eurodollar from the equation and get nearly the same performance. In essence, the results are unintentionally biased (curve fit) to natural gas. An average $150 winning trade in the Eurodollar is not going to offset an average $2000 losing trade in natural gas!

We recommend trading a basket of commodities for diversification, however, if traders do not normalize the data and most of the profits and losses arise from a few of the markets in the portfolio then that is not diversification. The problem is that going forward; traders are going to be dependent on those few markets to perform. It is far better knowing that any market has the potential to perform at an equal level rather than being dependent on markets in that portfolio.

It is likely that most trading systems ignore position sizing, or use it illogically because the design of most software packages is to work with a single contract based test. Of the numerous back testing products available for sale, we are only aware of two software packages that can properly do position sizing and money management testing. There are many products that claim to do it, but we have found that almost all these products do not do position sizing & money management correctly (there are many reasons for this, contact us for details). We use Bob Spears state-of-the-art testing software Mechanica (which sells for $25,000 a copy) for most position sizing based research and testing.

Other problems include vendors that only report the smaller drawdown numbers like “closed trade” drawdowns or “average annual” drawdowns. There are also problems with position sizing concepts such as “Optimal F” or “Fixed Ratio”. We feel both of these are just a dangerous form of hindsight biased curve fitting.

Another common fallacy says that traders should find their “best” single contract based trading system FIRST and THEN apply position sizing to it. This is not the correct approach; position sizing can change the risk-to-reward profiles of a single contract based trading system. A trading system that looked terrific, with a smooth equity curve on one contract basis, can look far less attractive when all markets are equally weighted for robustness.

Our Trading System & Position Sizing

For all the reason cited above, we develop trading systems with proper position sizing logic. We believe this raises the robustness and significance of the testing results. This also helps avoid the inadvertent optimizing that can occur with other types of position sizing / money management based testing software.

Dean Hoffman
Relativity Trading System

As worldwide demand for commodities continues to heat up and more investors (institutional and individual) start seeing commodities as a sensible investment vehicle, we expect this trend to continue. This growth has also raised the need for ways to select a Commodity Trading Advisor. In this article, we will outline what we believe are some of the best tools, and methods available to the individual investor when choosing a managed futures product.

Managed Futures Defined

Let’s first define what managed futures are and what they are not. Managed futures are not stocks or ETF’s that just invest in commodities. Managed futures accounts are investments in which funds invest in mainly leveraged, future dated contracts for commodities or financial instruments. Commodities can include sectors such as food, energy, raw materials and financial instruments like interest rates and stock indices.

The leverage, risks and rewards can be (but are not always) substantially higher when investing in the futures markets vs. the stock market. The National Futures Association and the Commodity Futures Trading Commission regulate managed futures investments in the United States (unless the firm / fund have “exempt” status). Regulated firms hold a Commodity Trading Advisors (CTA’s) or Commodity Pool Operators (CPO’s) license, but keep in mind, just because a firm carries a license is in no way an endorsement of future performance. Futures trading can carry large potential risks and is not for everybody. Investors should be familiar with all the risks before investing.

Finding lists of potential managers to sort through is fairly easy if investors know where to look. Firms such as Barclays Trading Group, Stark Research, Autumn Gold and Altegris Investments have databases of manager information available. One resource we like is www.autumngold.com. AutumnGold summarizes a free (with registration) online database of over 450 programs. Also, the programs can be sorted by a wide range of parameters such as minimum account size, funds under management, and various performance measurements.

The only problem we see with the online databases is that it can become somewhat overwhelming to try and narrow down your choices to just a handful of managers. To help simplify the process, we would like to share what we think are some of best performance metrics.

Managed Futures Evaluation

The first recommendation is to forget return! The least significant statistic often is a manager’s return. How can that be? What matters is RISK ADJUSTED RETURN. Just because somebody bet the farm and got lucky does not mean it was a nifty idea. Sooner or later (most often sooner) the inevitable wipe out will occur with a manager betting too aggressively.

There are many traditional risk adjusted return measurements, the most popular of which being the Sharpe ratio. The Sharpe Ratio compares the return relative to the underlying volatility in the investment. Although we are in agreement with the Sharpe Ratio’s logic, we feel it has one serious flaw. The flaw is that the Sharpe Ratio only views past volatility and does not try and predict future volatility. As a result, we feel the Sharpe ratio does not give an adequate view of the potential risks involved in a program.

A good example of this comes from the world of the “option writers” (those who sell options). Since most options end up expiring worthless, it is not uncommon for managers that sell options to have excellent Sharpe Ratios. They can have smooth looking equity curves that have produced for many years, but just because an equity curve looks smooth and consistent does not mean it will stay that way. What happened is meaningless if new investors do not have the same results. Option sellers with longer term excellent track records tend to have quick, spectacular “blowups”. The problem is that past volatility is not a reliable predictor of future volatility.

What is a reliable predictor? One of the best volatility predictors is the “Margin to Equity Ratio” (MTE). The MTE tells an investor roughly how much of their investment would be used for margin purposes. This number will vary day-by-day for a given manager, but investors can get the average range. If, for example, a managers MTE is 10%, this means that for every $100,000 invested the manager uses about $10,000 of this for margin. Keep this in mind; the exchanges set margin based on their approximations of risk. The higher the exchange perceives the risk in a contract the higher the margin they set. We encourage thinking just like the exchanges and raise the expectations for potential risk as the MTE goes higher. If we go back to the example of the option writers with exceptional Sharpe ratios, investors will also see that they often have high MTE ratios. We believe that these high MTE ratios were the tip off that could have avoided many disastrous scenarios. Once again, just as the exchanges often raise margin requirements as their expectation of volatility rises, so too do we see the potential for volatility (risk) to be higher as the MTE rises.

Another important use of the MTE comes down to pure math. If there were two managers that made $30,000 returns, yet one used $30,000 in account margin to do it, and the other used $60,000 in account margin to do it, then the results are different. Based on margin usage one manager’s return was twice as high as the others. This is essential to keep in mind, because often managers can appear to have similar performances, but when digging down into their margin usage investors will see large differences.

What is an ideal MTE? We do not like to see margin to equity ratios much above 10%. This is on the low end of the spectrum for managed futures accounts and cuts out most managers. Although it is true that low MTE ratios are no guarantee of lower risk, we feel that, at the minimum, it is possibly a decent gauge of sound risk management. Once again, we believe that as the MTE rises so does the potential for risk. There is also a related risk measurement often referred to as “portfolio heat” that uses similar concepts.

In summary, what we suggest is that potential investors compute returns not based on what the manager reported, but rather based on the return on margin (risk and drawdown should also be computed the same way). This will level the playing field and allow an apples-to-apples comparison. We are also in favor of being on the conservative side of the MTE spectrum, for us that means that we would likely reject any manager with a ratio above 10%. Using this method can help narrow down the list of choices to a manageable number rather quickly. After doing this then look and compare all the other risk adjusted performance measures and further refine your selection. (At this risk of this article being too long, we will save the other risk adjusted performance measurement discussions for future installments).

We want to caution once again that, in the end, no measure is a guarantee or assurance against risk or losses. Past performance is not always indicative of future results. Futures’ trading involves risks and is not for everybody. We are simply sharing what we feel is the best method by which to select a manager.

Dean Hoffman
Hoffman Asset Management
Tag: Managed Futures

Managed Futures – Emerging CTA Benefits

Investing with emerging managed futures CTAs can have benefits such as better risk adjusted returns. This is attributable to emerging CTA’s not being weighed down by their size. Specifically, emerging CTA’s can move into and out of markets easier and are able to trade markets that are not liquid enough for large CTA’s. My research has clearly shown me that being able to trade more markets is a tremendous benefit. Large CTA’s are confined to only trading markets such as financial instruments, energies and metals. They end up missing out on opportunities in many other traditional commodity sectors such as grains like soybeans, corn and wheat and foods such as coffee, sugar, cocoa, cattle, pork and fibers like cotton. Once again, missing out on these markets can come at a great price.

An added benefit with emerging CTAs includes smaller minimum account sizes. For example, Hoffman Asset Management Inc. will trade a diversified portfolio of over 70 markets with a minimum account size of only $125,000. This is in contrast to more established CTAs, they usually have minimums of $1,000,000 or more. .

Managed Futures – Summary of Emerging CTA Benefits

In summary, when you combine the benefits of potentially better performance and smaller minimum account sizes you can see that the emerging CTA can represent the ideal solution for many investors.

www.RelativityTradingSystem.com

Dean Hoffman
Tags: Managed Futures

Diversified trend followers offering individually managed futures accounts seemed to have minimums that were usually at least $1 million.

In the futures markets, small managed futures accounts—those with less than $250,000—face considerable challenges seldom experienced by large accounts. Considering that most commodity futures contracts have face values in the tens or hundreds of thousands of dollars, it is easy to surmise that these contracts are for large accounts. This is not so. Low-margin requirements have long attracted smaller speculators and, can be the proverbial rope with which to hang oneself.

Why Large Managed Futures Accounts Tend to do Better

Let’s analyze why large managed futures accounts may have it easier than small accounts.

First, large managed futures accounts can afford to trade almost any opportunity at any time. There are over 100 tradable commodity markets worldwide and should buy or sell opportunities simultaneously become available in any or all them, a large managed futures account can easily afford the margin and exposure. When it comes to investing, it is said that diversification is the only truly “free lunch.” Large managed futures accounts can afford to diversify with impunity. This is in stark contrast to small managed futures accounts where prudence dictates only having risk and exposure in a few markets simultaneously.

A large managed futures account does not have to shy away from any trading opportunities which may become available, even those whose volatility is fairly high. For example, a London copper trade with a stop loss $14,000 away represents a risk of only 1.4% in a million dollar managed account, but in a smaller managed account of only $100,000, this same trade would represent a risk of a whopping 14%! With such a large risk, stemming from the small size of the account, any sensible trader would be forced to avoid this trade. Having to skip these types of opportunities is yet another penalty paid by the small managed futures account.

On top of this, a large managed futures account can use one of the easiest forms of risk control available: contract scaling. Let’s assume, for example, that a trader has a large account which is long 50 gold contracts during a large bull market run and that they wish to cut their open trade profit exposure. To do this, they can simply scale out as many contracts as they need to lock in profit, all while maintaining their profitable position.

What can the small managed futures account do for scaling out, on the other hand, if he only has one contract in the first place? Once again, the small managed futures account does not enjoy the flexibility to control risk in the same fashion as the large managed futures account.

Now, despite all the negative points that I’ve just summarized above, I still believe that smaller accounts have advantages over large ones. Small accounts are able to trade markets that would be far too illiquid for large accounts. Most institutionally sized funds are almost confined to the trading of financial and energy instruments. They end up missing out on trading opportunities afforded by the traditional physical commodity markets, specifically commodities like grains, foods and fibers. This creates a lack of diversification and an over reliance on those few sectors in which the large account can trade.

The ironic thing about it is that many small accounts end up with this same problem. This is because they have chosen to deal with their small account problem by only trading in a few markets. Some even confine themselves to just one market and so end up missing out on the greatest advantage they have over the “big boys.”

Managed Futures with Hoffman Asset Management

It is for those smaller traders who want the advantages of true global diversification with individually managed, not pooled, accounts that we formed Hoffman Asset Management. Hoffman Asset Management, or HAMI, is carving out a unique niche by offering a managed account program that monitors and trades more than 70 diversified commodity markets. We do this while trading accounts as small as $30,000.

The intent of HAMI’s program’s design is to keep drawdowns and volatility in line with what has previously only been available with a large, widely diversified account. This combination of trading many markets within a small account while keeping volatility in check is truly unique. It fills what we feel is a tremendous void in traditional managed account offerings.

Although what we do is largely proprietary, the basic premise uses a form of relativity. HAMI monitors a large universe of tradable commodities for opportunities, yet is still highly selective in those trades that it will take. For roughly every 10 trading opportunities identified by HAMI’s combination of trading systems, it takes only 1. HAMI’s algorithms consider not only the direction of the market and its movement potential, but also how that potential ranks on a risk-adjusted basis.

Simply put, the idea is that an opportunity can only be evaluated relative to what else is available. For example, how does a trader know if a 5% return is acceptable or not? For a wise trader, the only acceptable answer is that it depends on what else is available. In other words, the acceptability of a 5% return can only be calculated based on the other relative options that are available. Only some of all the markets tracked by HAMI’s strategy get identified as the best and HAMI will consider only those few markets should one of its many trading systems generate a signal.

The portfolio selection process is dynamic and gets rebalanced every day. From day to day there are changes made to the basket of markets that we will consider trading. This keeps HAMI’s trades limited to only those markets that we feel have the best risk adjusted potential, and it allows us to evaluate a large portfolio while still keeping trades and margin requirements low.

Monitoring a large portfolio is key. If traders limit themselves to a predetermined small portfolio, how will they know that those markets are still the best markets? Hindsight bias portfolio selection is a form of curve fitting and is a leading downfall of many traders.

If an exceptional opportunity develops in a market outside his predetermined portfolio, any trader in his right mind would want to take advantage of it. By trading with Hoffman Asset Management’s trading systems, traders have the assurance that no market is arbitrarily ruled out if it has the potential to perform well. With Hoffman Asset Management’s trading systems, the likelihood that a portfolio is merely the product of past performance, or curve fit, considerations are vastly cut.

The key to doing these things is researched logic that can do all this automatically—and that is the secret to Hoffman Asset Management’s trading strategy.

Please feel free to contact us for more information.

Dean Hoffman
President

Commodity trading carries risk and is not suitable for all investors. Past performance is not indicative of future performance.

Tags: Managed Futures

The primary problem with optimizing past performance is that markets change. A low-volatility market suddenly becomes a high-volatility market. A market prone to trends becomes a choppy directionless market or, a market that previously had high leverage becomes a market with low leverage. The list is endless.

What tends to happen is that market X will tend to start acting like market Y, and market Y will tend to start acting like market Z. If a trader has thoroughly optimized his system to trade market Z, then he will be in trouble when it starts to trade like market X! This is a problem with many trading systems, usually stock index systems that tend to be optimized to one market or sector. Despite of their occasional impressive looking results, there’s some poison in their mix.

Contrast this last scenario with one in which the trading systems design works well with most all the markets, A thru Z. Now, it will not matter if market Z starts to act like market Y or market A starts to act like market P. They can change as many times as they want because the trading systems design will be universally robust with most ALL the various markets. Once again, the market characteristics can reshuffle countless times and the trading system acts like a Swiss army knife that has proved during historical testing it can deal well with most all those scenarios.

A few tip offs to an optimized Trading System

1. Unrealistic looking performance
2. Only trades one market or sector well
3. Uses different rules (algorithms) for each market
4. Uses different inputs for each market
5. Uses different rules or inputs for entering buys and sells
6. Does not factor in realistic transaction costs (slippage & commission)
7. Uses money management methods that do not include market normalization (like single contract performance only)
8. Uses static numbers for all markets like a $2000 stop or $5000 profit target (some markets could hit those in an hour and others could take weeks).

An important feature of a robust trading system is that it should weight every market equally. The testing should be done in a way that “normalizes” the difference between the markets. For example, natural gas changes an average of a few thousand dollars a day for each contract; however, Eurodollars change an average of a few hundred dollars a day for each contract. Traders need a way to balance and normalize this difference in testing.

The reason traders need to do this is that what if the trading system meets most of the above non-optimized rules, but it is trading one natural gas market contract for every one Eurodollar contract. The trading system would look best if it had many natural gas winners, but what it natural gas starts to have many losing trades and the Eurodollar starts to have many winning trades? Will a few, hundred dollar winning trades in one Eurodollar contract be enough to offset a few THOUSAND dollar losing trades in one natural gas contract?

If a trader is trading 20 markets, it is to get diversification, but if he is trading them all on a one contract basis then he is not diversified. Traders might have 25% of their portfolio making up for 90% of the profits and losses! The problem is that moving forward they will be dependent in those markets. It is far better not to be dependent on any given market in the portfolio. They should all be of equal weight and importance.

A Robust Trading System Should

1. Trade a portfolio of EVERY commodity market
2. Trade that large portfolio over a long test period
3. Use the same rules for every market
4. Use the same input values for every market
5. Have the same logic for entering buys and sells
6. Factor in realistic transaction costs
7. Normalize the markets for risk

After all this, the final step would be to do some walk-forward testing. This means, test and develop a trading system on data up until year 2000 (for example). Then after doing all the testing, see how it would have done from year 2000 until now. This helps avoid many benefits of hindsight. These are all things DH Trading Systems does when developing a trading system.

However, despite this wide array of information that must be considered, there is indeed one report that I have come to rely on more than any other report. This report has given me more comfort and confidence as a system trader than any other report. If I know that a system is properly created, I can almost use this report alone to decide about trading it! So what is this report? It is a “Start Trade Report”.

A Trading Systems Start Trade Report

In my opinion, a Start Trade Report can give the most robust, three-dimensional view possible of a trading system. It cuts through many pitfalls that come with traditional analysis and gets right down to the genuine heart of the matter. It even cuts through all the nonsense that comes up when looking at real-time performance.

I know what traders are thinking. I can hear it now. “Wait a minute, how can real-time performance be argued with?” Well, let me give an example that clearly illustrates this point, using one of my systems: Synergy.

In May of 2003, Synergy started a trade in London Copper. This trade became the most successful trade of the year. As of this writing (March 7th, 2004), this one trade has made profits of over $25,000 a contract.

Now, if a trader were using position sizing he might trade 2 or 3, or even more, of these, but here’s the thing: had they started a week or even a day after this trade was first made, they would have missed it entirely! Two investors trading the same system with the same investment size and the same money management rules could show a difference in their accounts of $25,000, $50,000, $75,000, or an even greater, more preposterous amount! They may have only started one day apart!

This can create tremendous confusion. One broker’s real time accounts can inexplicably appear to be far different from the real-time accounts of another broker, even when using the same trading systems.

Misleading Trading Systems Reporting

This phenomenon can also be used for dishonest or disingenuous purposes. It is possible for a trading systems vendor to simply cherry pick the best historical starting date to suit his purposes. He can choose a date right before a huge winner, or a series of winners. This can cause it to look as though the system needed little original starting capital and the return on invested funds was enormous. Choosing this date would mean that the first wins financed the rest of the trading.

But what if trading had started on a different date? What if that trader had even started on a date that was right before a series of losers? He might have needed 2, 3, or even 4 times the starting capital than would be needed had he started on a different date. His returns on the invested capital would be much less. In the worst-case scenario, he might have lost his entire investment before earning the profits shown.

Even if a broker or vendor shows an average of several of his accounts, this can still be a meager view and offer less than the needed amount of information. Theoretically, he could still cherry pick the starting dates of all 3 or 4 accounts, using each to show as much profit as possible. Alternately, he could have so few accounts to average from that the data suffers from what statisticians call a small sample size—not enough data to draw any valid conclusions.

An even worse offense would be if a disingenuous brokerage or vendor were to push some day trading systems because of the high frequency of trades and commissions they have the ability to generate, and then use some of his cherry picked “real time” accounts to “prove” that his strategy worked.

The point I am making is that there are countless ways that incorrect or intentionally altered start dates can impact performance, both in hypothetical reports and real-time performances. Traders need to rely on something better and more robust than much of what is currently available.

A Trading Systems Solution

What is the answer? Well, in my humble opinion, the answer to this is the Start Trade Report. The Start Trade Report runs tests on various systems as many as hundreds or thousands of times over the given period. It starts each test on a different date inside the period in which the trader could have made his new trades. For example, if there were 2,000 trades over a period of 10 years, the Start Trade Report will retest the system 2,000 times, each time starting on the date provided for each new trade.

The Start Trade Report also makes sure to reset the equity back to the original starting amount with each test. This is necessary because when using position sizing, traders may skip some trades in the beginning when the equity is still small, but, it is not correct to look at the results of trades that a trader would not have taken. I have sometimes seen brokerage firms report on trades generated by my system that, based on their account size, many of my clients would not have taken. I have seen, for example, a $3,500 losing trade in a system where most clients would have skipped any trade with a risk above $2,000. The Start Trade Report knows which trades to skip and at which times based on the starting capital of the traders.

This report can also allow traders to evaluate performance based on the margin required rather than account size. This feature allows traders to see the entire spectrum of ALL the possible outcomes rather than just one.

Trading Systems Start Trade Report Summary

Here are a few things that a Start Trade Report can show traders:

1. What percentage of the first 12 months was profitable based on the 2,000 different starting dates?
2. What was the average first year performance of the system when averaged over the 2,000 different possible starting dates?
3. How much money would my account have needed to contain if, theoretically, I started on the worst possible date?
4. What would be the average account size needed to trade this system based on the 2,000 different possible starting periods?
5. What would be the average amount that I went under my original starting point? What about the largest amount possible over all 2,000 different dates?

This report allows traders to filter out much of the garbage found in typical performance reporting. The Start Trade Report can filter out many errors in reporting “real time” performance based on either a sample size that is too small or starting dates and accounts that are “cherry picked.”

I hope traders can see that this information is invaluable. I honestly do not know how a trader could ever trade any trading systems without it. When investors look at a system in this much detail, it will be surprising to them how much confidence this report can build, not to mention the comfort. Ever since my early days of trading, this report was the one that gave me the most peace of mind. It was the only report that comforted me when there were drawdowns. It allowed me to know whether we were in the normal ranges of the bell curve, or whether we were going through something extreme. It also gave me a realistic range of outcomes to expect in the first year of trading.

We believe that providing traders with these reports will not only give them an incredible edge, but also build their confidence immeasurably. Confidence is a valuable attribute for a trader to have when the inevitable drawdown comes. In my personal experience, it is thanks to these reports that I am able to remain calm even during the worst of times.

To get a copy of the Start Trade Report please send us an email.

Dean Hoffman
DH Trading Systems

Trading Systems Optimization Failure

“What happened?” they ask. The answer is that what they created was likely a system that was a statistical coincidence (known as a “curve fit”). Curve fitting is where you force a system to conform to historical data. The problem is that the markets will behave much differently moving forward; therefore, a “perfect” trading system could be useless. For example, your computer finds the perfect dates historically to have bought and then sold the market. These dates are likely coincidental and have no future value, yet sometimes people will base a system on them. This is a clear example; however, most curve fits are some complex form of this basic concept.

Let’s look at another flawed example. Assume we wanted to optimize nickels that were most likely to land on heads. What we could do is flip millions of nickels and only select those that landed on heads. Then, we can take those remaining nickels and flip them again, once again only choosing those that land on heads. We could repeat this process over and over again each time only choosing those nickels that land on heads. At this point, we might conclude that we had narrowed down our nickels to only a small handful that were “optimized” to land on heads. We could then go out and wager large bets with those nickels putting all our money on heads. We would quickly make a fortune, right? WRONG!

We would quickly lose our money. Those nickels were not optimized for heads; they always had 50/50 odds. What might have confused some is that they thought they had found predictable nickels. All they found was a statistical coincidence!

Trading Systems Optimization Reality

Because there is so much data, and so much computing power available, these kinds of errors find their way into trading systems all the time. When developing a system it is imperative to avoid optimizing as much as possible. You need to find NON curve-fit, robust systems. There can be a place for some types of optimizing, but it must be handled correctly.

We design all our systems in a robust way that we feel avoids the optimization pitfalls of so many other systems.

Feel free to email or contact us with any questions or comments on this subject.

Dean Hoffman

We have talked about the dangers of optimizing trading systems (forcing trading systems to conform to historical data), but, one of the subtler forms of optimization happens with portfolio selection. This happens when trading systems are only shown tested across a handful or a small number of markets (or sometimes just one market).

The problem is that what is usually done is that most all the available markets get tested, and then only those that performed the best get shown in the portfolio. This is an enormous mistake, because the markets that performed best historically are rarely the ones that continue to be the best. What traders end up with is something that only worked well historically.

Trading Systems Solution

To avoid this tendency, we believe that the most robust way to see a system test is across ALL the available markets. Some will argue that different markets should be traded different ways, and to that we say NONSENSE. Markets are always changing, and a market that traded like market XYZ today will trade like market ZYX tomorrow. Unless a system is robust enough to trade every market, it is likely a useless curve fit of the data.

For us in the commodities markets, we use test roughly 80 markets. There are over 100 commodity contracts that trade, but we do limit the selection to those that are liquid enough (have enough trading volume) to trade.

Testing this way does cause one problem. The problem is that traders can be trading many markets in the same sector at a given time. Investors will need to have some sector risk control mechanisms in place. We like to be certain that the risk in a given sector does not exceed about 5% of the account equity.

If someone creates a system that can successfully trade nearly EVERY commodity market and uses the same rules for each market and gets tested over a long period, he may be on to something. Just remember, the next time someone shows the results of a backtest ask him “How many markets does this test include?” If the answer (for commodities) is less than about 70 or 80, then be suspicious that this may be curve fit results. Once again, curve fitting tends to produce systems that ONLY perform well historically.

DH Trading Systems Approach to Trading Systems.

We test all the systems we make available across nearly every tradable commodity market. We could easily improve performance (historically) by only cherry picking the best markets, but we know this is misleading data.

For more information about DH Trading Systems feel free to contact us.

Dean Hoffman
www.RelativityTradingSystem.com
DH Trading Systems

However, it is valuable to know that frequently the investor can start with less than the minimum through notional funding. For example, an investor could notionally fund a managed futures or trading systems account at the $50,000 level but tell the manager to trade at a nominal $100,000 level. In other words, the account will trade as though there were $100,000 in it even though there is not. The investor is simply making use of added leverage.

In the previous example this means that the account will be trading at 2-to-1 leverage. Meaning the investors will have gains and losses at twice the level. Had the investor only put up a third of the nominal amount minimum then he would see gains and losses at 3 times the level and so on.

Why those using Trading Systems or Managed Futures Might Want to Consider Notional Funding

Notional funding can be an efficient use of capital, because frequently trading systems or managed futures accounts will not come anywhere close to using all the money in the account. For example, in Hoffman Asset Management’s case we have a margin-to-equity ratio of generally less than 10%. What this means is that for every $100,000 invested, generally speaking, we will be using less than $10,000 at any given time for margin. The remaining $90,000 sits on the sidelines stagnant. Although it is true that interest on those unused funds can be earned, most investor’s feel they could do better investing those funds elsewhere. Often time’s high net worth individuals or institutions will even put NOTHING in their accounts and trade 100% notionally. The question for investors should be “how can I calculate a reasonable notional level to invest at”.

We feel the answer to that question is one that can be computed based on several statistics. Specifically, what is the maximum drawdown expected and what is the maximum margin that might be needed. For example, Hoffman Asset Management (as of this writing) has had a maximum drawdown of about 17% on a $125,000 nominal account size. This means a $21,250 drawdown in cash terms. The maximum margin usage is about 15% on $125,000 or, about $18,750 in cash terms.

To compute a notional investment amount, we suggest that an investor add the maximum expected drawdown and the maximum expected margin usage. This figure would give the investor the absolute minimum they could invest in the account without having a margin call.

In the previous example, if an investor had started on the worst possible day, and had a $21,250 drawdown, and simultaneously had the maximum margin usage of $18,750, he would have needed $40,000 of cash in the account to fund that $125,000 nominal account size. Once again, some institutions and individuals who are not worried about margin calls may even decide to fund the account with less than that (or zero).

Benefits to the Trading Systems or Managed Futures Investor

This allows for the smaller, but more aggressive investor to participate in the program without needing to tie up the entire amount in cash. This will amplify their gains and losses at the added leverage level they are using. If, for example, the manager made a 30% return with a 17% drawdown, then the investor at 2-to-1 leverage would have experienced 60% gains with a 34% drawdown.

Once again, this is a more aggressive approach, and we recommend this only for investors who fully understand the benefits and risks of notional funding. However, for the right investor, this can be a valuable tool to have in his or her arsenal.

Dean Hoffman
DH Trading Systems and Managed Futures Accounts

Commodity trading carries risks and is not for everybody. Past performance is not necessarily indicative of future performance.